Bootstraps for Meta-Analysis with an Application to the Impact of Climate Change
AbstractBootstrap and smoothed bootstrap methods are used to estimate the uncertainty about the total impact of climate change, and to assess the performance of commonly used impact functions. Kernel regression is extended to include restrictions on the functional form. Impact functions do not describe the primary estimates of the economic impacts very well, and monotonic functions do particularly badly. The impacts of climate change do not significantly deviate from zero until 2.5-3.5°C warming. The uncertainty is large, and so is the risk premium. The ambiguity premium is small, however. The certainty equivalent impact is a negative 1.5% of income for 2.5°C, rising to 15% (50%) for 5.0°C for a rate of risk aversion of 1 (2).
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Bibliographic InfoPaper provided by Department of Economics, University of Sussex in its series Working Paper Series with number 6413.
Date of creation: Sep 2013
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impacts of climate change; kernel regression; bootstrap; risk aversion; ambiguity aversion;
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-09-24 (All new papers)
- NEP-ENE-2013-09-24 (Energy Economics)
- NEP-ENV-2013-09-24 (Environmental Economics)
- NEP-UPT-2013-09-24 (Utility Models & Prospect Theory)
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